GPS displacement dataset for study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from GPS measurements poses challenges, especially when dealing with large datasets with continental or global coverage. This study recommends a method for preparing and processing GPS measurements for use in a future joint solution with GRACE(-FO)-like observations. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS timeseries is a critical step, which identifies a) corrections that were missed; b) sites that contain non-elastic signals (e.g., close to aquifers); and c) sites affected by background modelling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical land displacement (VLD) estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of VLD, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty derived using each technique and find that three techniques exhibit an average noise level between 2–3 mm: white noise, flicker noise, and RMS of residuals about a seasonality and trend fit. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatial correlated noise. The new data set is suitable for use in a future joint solution with GRACE(-FO)-like observations.